code: def forward(self, x):
      if self.training and self.p > 0.0:
          keep_prob = 1.0 - self.p
          # per data point mask
          mask = torch.zeros(
              (x.size(0), 1, 1, 1), device=x.device
          ).bernoulli_(keep_prob)
          return x / keep_prob * mask
  
      return x
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the DropPath obj's underlying member method code with example usage: obj.forward(x) and returns: x, unknown